Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Das, Shreya, Kumar, Kundan, Iqbal, Muhammad, Savolainen, Outi, Baumann, Dominik, Ruotsalainen, Laura, Särkkä, Simo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Communication-Efficient Distributed Kalman Filtering using ADMM
by: Iqbal, Muhammad, et al.
Published: (2025)
by: Iqbal, Muhammad, et al.
Published: (2025)
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
by: Kumar, Kundan, et al.
Published: (2025)
by: Kumar, Kundan, et al.
Published: (2025)
A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control
by: Iacob, Casian, et al.
Published: (2024)
by: Iacob, Casian, et al.
Published: (2024)
Dyna-Style Reinforcement Learning Modeling and Control of Non-linear Dynamics
by: Abdelsalam, Karim, et al.
Published: (2025)
by: Abdelsalam, Karim, et al.
Published: (2025)
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
by: Guo, Qingyun, et al.
Published: (2026)
by: Guo, Qingyun, et al.
Published: (2026)
Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
by: Sheng, Xinyi, et al.
Published: (2025)
by: Sheng, Xinyi, et al.
Published: (2025)
Online Bayesian Experimental Design for Partially Observed Dynamical Systems
by: Pérez-Vieites, Sara, et al.
Published: (2025)
by: Pérez-Vieites, Sara, et al.
Published: (2025)
Predictive Safety Shield for Dyna-Q Reinforcement Learning
by: Pin, Jin, et al.
Published: (2025)
by: Pin, Jin, et al.
Published: (2025)
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
by: Zhang, Tianqi, et al.
Published: (2025)
by: Zhang, Tianqi, et al.
Published: (2025)
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
by: Li, Yansong, et al.
Published: (2024)
by: Li, Yansong, et al.
Published: (2024)
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors
by: Rap, Jake, et al.
Published: (2025)
by: Rap, Jake, et al.
Published: (2025)
Towards safe control parameter tuning in distributed multi-agent systems
by: Tokmak, Abdullah, et al.
Published: (2025)
by: Tokmak, Abdullah, et al.
Published: (2025)
Safe Bayesian optimization across noise models via scenario programming
by: Tokmak, Abdullah, et al.
Published: (2025)
by: Tokmak, Abdullah, et al.
Published: (2025)
PACSBO: Probably approximately correct safe Bayesian optimization
by: Tokmak, Abdullah, et al.
Published: (2024)
by: Tokmak, Abdullah, et al.
Published: (2024)
DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
by: Aftabi, Navid, et al.
Published: (2025)
by: Aftabi, Navid, et al.
Published: (2025)
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
by: Cheng, Xiaoyuan, et al.
Published: (2026)
by: Cheng, Xiaoyuan, et al.
Published: (2026)
Safe learning-based control via function-based uncertainty quantification
by: Tokmak, Abdullah, et al.
Published: (2026)
by: Tokmak, Abdullah, et al.
Published: (2026)
Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
by: Braniff, Austin, et al.
Published: (2025)
by: Braniff, Austin, et al.
Published: (2025)
Safe exploration in reproducing kernel Hilbert spaces
by: Tokmak, Abdullah, et al.
Published: (2025)
by: Tokmak, Abdullah, et al.
Published: (2025)
Simulation-Aided Policy Tuning for Black-Box Robot Learning
by: He, Shiming, et al.
Published: (2024)
by: He, Shiming, et al.
Published: (2024)
Structured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systems
by: Qian, Jiajun, et al.
Published: (2024)
by: Qian, Jiajun, et al.
Published: (2024)
HyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural Networks
by: Gornet, Jonathan, et al.
Published: (2025)
by: Gornet, Jonathan, et al.
Published: (2025)
Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
by: Braniff, Austin, et al.
Published: (2026)
by: Braniff, Austin, et al.
Published: (2026)
Safety and optimality in learning-based control at low computational cost
by: Baumann, Dominik, et al.
Published: (2025)
by: Baumann, Dominik, et al.
Published: (2025)
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
by: Takubo, Yuji, et al.
Published: (2021)
by: Takubo, Yuji, et al.
Published: (2021)
Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
by: Spoor, Lindsay, et al.
Published: (2025)
by: Spoor, Lindsay, et al.
Published: (2025)
Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
by: Syed, Shahbaz P Qadri, et al.
Published: (2025)
by: Syed, Shahbaz P Qadri, et al.
Published: (2025)
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
by: Nasiri, Mahdi, et al.
Published: (2024)
by: Nasiri, Mahdi, et al.
Published: (2024)
NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks
by: Rajagede, Rian Adam, et al.
Published: (2025)
by: Rajagede, Rian Adam, et al.
Published: (2025)
Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty
by: Enwerem, Clinton, et al.
Published: (2026)
by: Enwerem, Clinton, et al.
Published: (2026)
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning
by: Basso, Davide, et al.
Published: (2024)
by: Basso, Davide, et al.
Published: (2024)
Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
by: Das, Goutam, et al.
Published: (2026)
by: Das, Goutam, et al.
Published: (2026)
Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
by: de Vargas, Jan Marco Ruiz, et al.
Published: (2026)
by: de Vargas, Jan Marco Ruiz, et al.
Published: (2026)
A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
by: Liu, M. Vivienne, et al.
Published: (2023)
by: Liu, M. Vivienne, et al.
Published: (2023)
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
by: Barbara, Nicholas H., et al.
Published: (2024)
by: Barbara, Nicholas H., et al.
Published: (2024)
RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems
by: Siyadatzadeh, Roozbeh, et al.
Published: (2025)
by: Siyadatzadeh, Roozbeh, et al.
Published: (2025)
Learning Mixtures of Linear Dynamical Systems via Hybrid Tensor-EM Method
by: Gong, Lulu, et al.
Published: (2025)
by: Gong, Lulu, et al.
Published: (2025)
Improving Mixed-Criticality Scheduling with Reinforcement Learning
by: El-Mahdy, Muhammad, et al.
Published: (2025)
by: El-Mahdy, Muhammad, et al.
Published: (2025)
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
by: Laiche, Ibrahim, et al.
Published: (2025)
by: Laiche, Ibrahim, et al.
Published: (2025)
A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning
by: Alibotaiken, Alimu, et al.
Published: (2025)
by: Alibotaiken, Alimu, et al.
Published: (2025)
Similar Items
-
Communication-Efficient Distributed Kalman Filtering using ADMM
by: Iqbal, Muhammad, et al.
Published: (2025) -
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
by: Kumar, Kundan, et al.
Published: (2025) -
A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control
by: Iacob, Casian, et al.
Published: (2024) -
Dyna-Style Reinforcement Learning Modeling and Control of Non-linear Dynamics
by: Abdelsalam, Karim, et al.
Published: (2025) -
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
by: Guo, Qingyun, et al.
Published: (2026)